The Value of Time in the United States: Estimates from Nationwide Natural Field Experiments

Abstract

The value of time determines relative prices of goods and services, investments, productivity, economic growth, and measurements of income inequality. Economists in the 1960s began to focus on the value of non-work time, pioneering a deep literature exploring the optimal allocation and value of time. By leveraging key features of these classic time allocation theories, we use a novel approach to estimate the value of time (VOT) via two large-scale natural field experiments with the ridesharing company Lyft. We use random variation in both wait times and prices to estimate a consumer's VOT with a data set of more than 14 million observations across consumers in U.S. cities. We find that the VOT is roughly $19 per hour (or 75% (100%) of the after-tax mean (median) wage rate) and varies predictably with choice circumstances correlated with the opportunity cost of wait time. Our VOT estimate is larger than what is currently used by the U.S. Government, suggesting that society is under-valuing time improvements and subsequently under-investing public resources in time-saving infrastructure projects and technologies

https://en.wikipedia.org/wiki/History_of_timekeeping_devices

Conclusion

Having gotten this far in our study you have surely invested a fair amount of time. We hope that such time was indeed an investment, and not ill-spent. This is because time is the ultimate scarce resource, and its value has deep implications for a range of economic phenomena and investment decisions. Our starting point is a literature from the 1960s that had deep implications for our understanding of the family, the household, and time allocation more generally. We leverage insights from these classic time allocation theories to provide a theoretically-consistent but updated approach to estimate the VOT. The theory carefully directs two large-scale natural field experiments on the Lyft platform to estimate the causal effects of wait time and price on ride-share demand.

We report several interesting insights. First, we estimate a VOT that is roughly $19 per hour (2015 prices). This estimate is 75-80% of the mean wage rate for the various regions in our experiment, which is quantitatively different from the findings of previous empirical studies on the VOT (Small et al., 2007) and is greater than the existing US policy guidelines on the VOT (USDOT, 2015). Second, we document that, consistent with standard microeconomic models (Becker, 1965; DeSerpa, 1971), the VOT is related to the opportunity cost of time, the available substitute set, and other key features of the trip that impact marginal benefits and marginal costs. Third, taken in aggregate, our research has key implications for policy. Specifically, we recommend that policymakers: (i) account for the great deal of VOT heterogeneity with respect to cities, locations within cities, day of week, and time of day; and (ii) adjust the rule-of-thumb VOT estimates up to 75% of the after-tax mean wage rate otherwise.

We view our VOT estimates as not only adding unique measures to a rich literature, but also providing a key link to the classic time allocation literature. Important areas where this research agenda goes from here can be found in caveats to our research. First, we do not examine the value of reliability in passenger travel or through using ridesharing companies, such as Lyft.

We acknowledge that this value could be important and separate from the VOT, but is beyond the scope of this paper. Future studies should consider the value of reliability—the extent to which waiting times vary about their mean—simultaneously with the VOT. On a rideshare platform, one possible approach would be to run a multi-modal waiting time and price experiment, and to model the passenger’s choice of not only whether to take a ride, but also of which ride mode (e.g., Classic or Shared) to take. Shared ride modes typically have more variable waiting times (today quoted as a range on the Lyft app), so such a cross-mode comparison might shed light on the value of reliability. Second, we focus on passenger travel in our data and ignore the VOT for rail, air, and freight travel. Combining the various travel modes and exploring their interplay is an ambitious research agenda that promises to lend deep positive and normative insights.

https://www.nber.org/papers/w28208

by Ariel Goldszmidt, John A. List, Robert D. Metcalfe, Ian Muir, V. Kerry Smith & Jenny Wang

National Bureau of Economic Research (NBER) www.NBER.org

Working Paper 28208; Issue Date: December, 2020